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Runs agentic coding assistants in docker containers

Project description

aicage

Run your favorite AI coding agents comfortably in Docker.

Why use aicage?

Agents need deep access (read code, run shells, install deps). Their built-in safety checks are naturally limited.

Running agents in containers gives a hard boundary - while the experience stays the same. See Why cage agents? for the full rationale.

Quickstart

  • Prerequisites:

    • Docker
    • Python 3.10+ and pipx
  • Install:

    pipx install aicage
    
  • Navigate to your project directory.

  • Use one of these commands:

    aicage codex
    aicage copilot
    aicage gemini
    aicage goose
    aicage opencode
    aicage qwen
    

Base images

The first run asks which base image to use; pick Ubuntu or whatever matches your Linux distro.

Base Distro Notes
ubuntu Ubuntu Good default for most users
debian Debian For Debian users
fedora Fedora For RedHat/Fedora users
alpine Alpine Minimal footprint; experimental
node Ubuntu Official Node image (all base images have Node)
act Ubuntu Default runner image from act (act runs GitHub Actions locally)

All base images have the same stack of tools installed.

Agents

CLI Agent Homepage
codex Codex CLI https://developers.openai.com/codex/cli
copilot GitHub Copilot CLI https://github.com/features/copilot/cli
gemini Gemini CLI https://geminicli.com
goose Goose CLI https://block.github.io/goose
opencode OpenCode https://qwenlm.github.io/qwen-code-docs
qwen Qwen Code https://opencode.ai

Your existing CLI config for each agent is mounted inside the container so you can keep using your preferences and credentials.

aicage options

  • --dry-run prints the composed docker run command without executing it.
  • --entrypoint PATH mounts a custom entrypoint script to /usr/local/bin/entrypoint.sh.
  • --docker mounts /run/docker.sock into the container to enable Docker-in-Docker workflows.
  • --config print prints the project config path and its contents.

Configuration file formats are documented in CONFIG.md.

Why cage agents?

AI coding agents read your code, run shells, install packages, and edit files. That power is useful, but granting it directly on the host expands your risk surface.

Where built-in safety is limited:

  • Allow/deny lists only cover known patterns; unexpected commands or attack paths can slip through.
  • Some agents work fully only after relaxing their own safety modes, broadening what they can touch.
  • “Read-only project” features are software rules. Other projects and files still sit alongside them on the same host.

How aicage mitigates this:

  • Containers create a hard boundary: the agent can access only what you explicitly mount. Day-to-day use stays familiar—just with the host kept out of reach.

Development info

More details are in DEVELOPMENT.md.

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